To determine the truth of the statements regarding the stopping times of students, we would typically need a distribution of the data or a graphical representation (like a histogram or box plot) that indicates the stopping times.
However, since you didn't provide any specific dataset or distribution details, I can explain how to evaluate each statement based on a common understanding of statistical data interpretation.
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½ of the students stopped at about 56 seconds or more:
- This statement implies that the median of the stopping times is around 56 seconds. If we had a distribution where the median is indeed 56 seconds, then this statement would be true. You would know this by checking the data for its median value.
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¼ of the students stopped at 35 seconds or more:
- This statement suggests that the 25th percentile (first quartile) of the data is around 35 seconds. If you can determine that indeed 25% of the students have stopping times above this value, this statement is true. You’d check this by analyzing the percentile data.
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¾ of the students stopped at 63 seconds or less:
- This statement indicates that 63 seconds is the 75th percentile (third quartile). To verify this, you would need to check how many students stopped at or below 63 seconds and confirm that this represents 75% of the total data.
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½ or 50% of the students stopped between 35 to 56 seconds:
- This statement means that the interquartile range (between the first and second quartiles or 25th and 50th percentiles) contains 50% of the students. To verify it, you would see if the data shows that the range from the 25th to 50th percentile captures half of the total observations.
To summarize, without access to the actual data or distribution values, we can’t definitively determine which, if any, of the statements are true. However, their validity can be assessed by looking at the medians, quartiles, and percentiles of the data. If given data points or a statistical summary, we would be able to directly verify each statement.